File size: 6,224 Bytes
503a2ae |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 |
# coding=utf-8
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
The data came from the GENIA version 3.02 corpus (Kim et al., 2003).
This was formed from a controlled search on MEDLINE using the MeSH terms human, blood cells and transcription factors.
From this search 2,000 abstracts were selected and hand annotated according to a small taxonomy of 48 classes based on
a chemical classification. Among the classes, 36 terminal classes were used to annotate the GENIA corpus.
"""
from typing import Dict, List, Tuple
import datasets
from .bigbiohub import kb_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks
_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
# TODO: Add BibTeX citation
_CITATION = """\
@inproceedings{collier-kim-2004-introduction,
title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}",
author = "Collier, Nigel and Kim, Jin-Dong",
booktitle = "Proceedings of the International Joint Workshop
on Natural Language Processing in Biomedicine and its Applications
({NLPBA}/{B}io{NLP})",
month = aug # " 28th and 29th", year = "2004",
address = "Geneva, Switzerland",
publisher = "COLING",
url = "https://aclanthology.org/W04-1213",
pages = "73--78",
}
"""
_DATASETNAME = "jnlpba"
_DISPLAYNAME = "JNLPBA"
_DESCRIPTION = """\
NER For Bio-Entities
"""
_HOMEPAGE = "http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004"
_LICENSE = 'Creative Commons Attribution 3.0 Unported'
_URLS = {
_DATASETNAME: "http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Train/Genia4ERtraining.tar.gz",
}
# TODO: add supported task by dataset. One dataset may support multiple tasks
_SUPPORTED_TASKS = [
Tasks.NAMED_ENTITY_RECOGNITION
] # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
# TODO: set this to a version that is associated with the dataset. if none exists use "1.0.0"
# This version doesn't have to be consistent with semantic versioning. Anything that is
# provided by the original dataset as a version goes.
_SOURCE_VERSION = "3.2.0"
_BIGBIO_VERSION = "1.0.0"
class JNLPBADataset(datasets.GeneratorBasedBuilder):
"""
The data came from the GENIA version 3.02 corpus
(Kim et al., 2003).
This was formed from a controlled search on MEDLINE
using the MeSH terms human, blood cells and transcription factors.
From this search 2,000 abstracts were selected and hand annotated
according to a small taxonomy of 48 classes based on
a chemical classification.
Among the classes, 36 terminal classes were used to annotate the GENIA corpus.
"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
BUILDER_CONFIGS = [
BigBioConfig(
name="jnlpba_source",
version=SOURCE_VERSION,
description="jnlpba source schema",
schema="source",
subset_id="jnlpba",
),
BigBioConfig(
name="jnlpba_bigbio_kb",
version=BIGBIO_VERSION,
description="jnlpba BigBio schema",
schema="bigbio_kb",
subset_id="jnlpba",
),
]
DEFAULT_CONFIG_NAME = "jnlpba_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.load_dataset("jnlpba", split="train").features
elif self.config.schema == "bigbio_kb":
features = kb_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=str(_LICENSE),
citation=_CITATION,
)
def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
data = datasets.load_dataset("jnlpba")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# Whatever you put in gen_kwargs will be passed to _generate_examples
gen_kwargs={"data": data["train"]},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"data": data["validation"]},
),
]
def _generate_examples(self, data: datasets.Dataset) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
uid = 0
if self.config.schema == "source":
for key, sample in enumerate(data):
yield key, sample
elif self.config.schema == "bigbio_kb":
for i, sample in enumerate(data):
feature_dict = {
"id": uid,
"document_id": "NULL",
"passages": [],
"entities": [],
"relations": [],
"events": [],
"coreferences": [],
}
uid += 1
offset_start = 0
for token, tag in zip(sample["tokens"], sample["ner_tags"]):
offset_start += len(token) + 1
feature_dict["entities"].append(
{
"id": uid,
"offsets": [[offset_start, offset_start + len(token)]],
"text": [token],
"type": tag,
"normalized": [],
}
)
uid += 1
# entities
yield i, feature_dict
|